A major problem of current deep learning models is that they make high confidence predictions when facing unexpected, OOD, data [1]}, [2]}, [3]}, like a knee X-ray.
In our example, prioritization based on false, high-confidence, OOD X-rays can lead to longer waiting times for other patients with time critical conditions, like a pneumothorax, potentially risking their life until the error is discovered and resolved.
Moreover, repeated instances of such misreporting will - if not balanced with transparency measures sufficiently - quickly lead physicians to distrust the model, eventually leading them refrain of using it [4]}, [5]}, [6]}.
